IBM Research is engaged in a research program in symbiotic cognitive computing to investigate how to embed cognitive computing in physical spaces. This article proposes 5 key principles of symbiotic cognitive computing.  We describe how these principles are applied in a particular symbiotic cognitive computing environment and in an illustrative application.  

This column provides an encounter with the Artificial Intelligence research community in the state of Israel. The first section introduces this community and its special attributes. The second section provides overview on some recent research projects done in Israel. The author serves as the chair of the Israeli AI association

This article reports on the first international Competition of Distributed and Multiagent Planners (CoDMAP). The competition focused on cooperative domain-independent planners compatible with a minimal multiagent extension of the classical planning model. The motivations for the competition were manifold: to standardize the problem description language with a common set of benchmarks, to promote development of multiagent planners both inside and outside of the multiagent research community, and to serve as a prototype for future multiagent planning competitions. The article provides an overview of cooperative multiagent planning, describes a novel variant of standardized input language for encoding mutliagent planning problems and summarizes the key points of organization, competing planners and results of the competition.

Customers at Royal Bank of Scotland and NatWest may soon be sorting out issues with help from a virtual chatbot. Web-based Luvo will be able to answer simple queries such as how to order a replacement card. Designed using IBM Watson technology, the virtual agent is able to understand and learn from human interactions. In future, Luvo may be able to understand if a customer was feeling frustrated or unhappy and change its tone and actions accordingly, IBM said. The service will initially be rolled out to RBS and NatWest customers, starting in December with about 10% of RBS customers in Scotland.

Customers at Royal Bank of Scotland and NatWest may soon be sorting out issues with help from a virtual chatbot. Web-based Luvo will be able to answer simple queries such as how to order a replacement card. Designed using IBM Watson technology, the virtual agent is able to understand and learn from human interactions. In future, Luvo may be able to understand if a customer was feeling frustrated or unhappy and change its tone and actions accordingly, IBM said. The service will initially be rolled out to RBS and NatWest customers, starting in December with about 10% of RBS customers in Scotland.

SEB will be the first bank to use IPsoft's cognitive technology for customer-facing operations in the Swedish language. The artificial intelligence (AI) solution, known as Amelia, will be integrated into the front-office set-up at SEB by the end of this year. The project follows on from a successful deployment of Amelia for SEB's internal service desk, supporting 15,000 staff. In the customer-facing role, Amelia will be dealing with one million clients of SEB. "Customer service is a key differentiator. By making Amelia available to respond to queries, we enhance our customers' flexibility of receiving individualised support at a time that suits them and without any delays in response," states Rasmus Järborg, chief strategy officer at SEB.

This paper develops an active sensing method to estimate the relative weight (or trust) agents place on their neighbors' information in a social network. The model used for the regression is based on the steady state equation in the linear DeGroot model under the influence of stubborn agents, i.e., agents whose opinions are not influenced by their neighbors. This method can be viewed as a \emph{social RADAR}, where the stubborn agents excite the system and the latter can be estimated through the reverberation observed from the analysis of the agents' opinions. The social network sensing problem can be interpreted as a blind compressed sensing problem with a sparse measurement matrix. We prove that the network structure will be revealed when a sufficient number of stubborn agents independently influence a number of ordinary (non-stubborn) agents. We investigate the scenario with a deterministic or randomized DeGroot model and propose a consistent estimator of the steady states for the latter scenario. Simulation results on synthetic and real world networks support our findings.

In this work we introduce Quixote, a system that makes programming virtual agents more accessible to non-programmers by enabling these agents to be trained using the sociocultural knowledge present in stories. Quixote uses a corpus of exemplar stories to automatically engineer a reward function that is used to train virtual agents to exhibit desired behaviors using reinforcement learning. We show the effectiveness of our system with a case study conducted in a virtual environment called Robbery World that simulates a bank robbery scenario. In this case study, we use a corpus of stories crowdsourced from Amazon Mechanical Turk to guide learning. We evaluate Quixote under a variety of different conditions to determine the overall effectiveness of the system in Robbery World.

This paper examines the performance of a number of AI agents on the games included in the General Video Game Playing Competition. Through analyzing these results, the paper seeks to provide insight into the strengths and weaknesses of the current generation of video game playing algorithms. The paper also provides an analysis of the given games in terms of inherent features which define the different games. Finally, the game features are matched with AI agents, based on performance, in order to demonstrate a plausible case for algorithm portfolios as a general video game playing technique.

It is quite difficult at first to precisely define'Complexity Science'. It is a new perspective of methodology and modeling approaches that are based more on reality than assumptions. Quite simply put, Complexity Science is a new way to grasp and manage reality. It does not study systems in isolation like gambling dice or planetary motion only. It studies the complex, holistic, inter-connected reality in which we actually live such as financial stock markets, social policies, economic policies, natural catastrophes and so on.